Artificial intelligence : a modern approach /
In this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in areas such as partially observable search, continge...
| Main Author: | |
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| Other Authors: | , , , , , |
| Format: | Book |
| Language: | English |
| Published: |
England :
Pearson Education Limited,
c2016.
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| Edition: | 3rd edition (Global edition) |
| Series: | Prentice Hall series in artificial intelligence
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| Subjects: | |
| Classic Catalogue: | View this record in Classic Catalogue |
Table of Contents:
- Artificial Intelligence:
- Introduction:
- What is AI?
- Foundations of artificial intelligence
- History of artificial intelligence
- State of the art
- Summary, bibliographical and historical notes, exercises
- Intelligent agents:
- Agents and environments
- Good behavior: concept of rationality
- Nature of environments
- Structure of agents
- Summary, bibliographical and historical notes, exercises
- Problem-Solving:
- Solving problems by searching:
- Problem-solving agents
- Example problems
- Searching for solutions
- Uniformed search strategies
- Informed (heuristic) search strategies
- Heuristic functions
- Summary, bibliographical and historical notes, exercises
- Beyond classical search:
- Local search algorithms and optimization problems
- Local search in continuous spaces
- Searching with nondeterministic actions
- Searching with partial observations
- Online search agents and unknown environments
- Summary, bibliographical and historical notes, exercises
- Adversarial search:
- Games
- Optimal decisions in games
- Alpha-beta pruning
- Imperfect real-time decisions
- Stochastic games
- Partially observable games
- State-of-the-art game programs
- Alternative approaches
- Summary, bibliographical and historical notes, exercises
- Constraint satisfaction problems:
- Defining constraint satisfaction problems
- Constraint propagation: inference in CSPs
- Backtracking search for CSPs
- Local search for CSPs
- Structure of problems
- Summary, bibliographical and historical notes, exercises
- Knowledge, Reasoning, And Planning:
- Logical agents:
- Knowledge-based agents
- Wumpus world
- Logic
- Propositional logic: a very simple logic
- Propositional theorem proving
- Effective propositional model checking
- Agents based on propositional logic
- Summary, bibliographical and historical notes, exercises
- First-order logic:
- Representation revisited
- Syntax and semantics of first-order logic
- Using first-order logic
- Knowledge engineering in first-order logic
- Summary, bibliographical and historical notes, exercises
- Inference in first-order logic:
- Propositional vs first-order inference
- Unification and lifting
- Forward chaining
- Backward chaining
- Resolution
- Summary, bibliographical and historical notes, exercises
- Classical planning:
- Definition of classical planning
- Algorithms for planning as state-space search
- Planning graphs
- Other classical planning approaches
- Analysis of planning approaches
- Summary, bibliographical and historical notes, exercises
- Planning and acting in the real world:
- Time, schedules, and resources
- Hierarchical planning
- Planning and acting in nondeterministic domains
- Multiagent planning
- Summary, bibliographical and historical notes, exercises
- Knowledge representation:
- Ontological engineering
- Categories and objects
- Events
- Mental events and mental objects
- Reasoning systems for categories
- Reasoning with default information
- Internet shopping world
- Summary, bibliographical and historical notes, exercises Uncertain Knowledge And Reasoning:
- Quantifying uncertainty:
- Acting under uncertainty
- Basic probability notation
- Inference using full joint distributions
- Independence
- Bayes' rule and its use
- Wumpus world revisited
- Summary, bibliographical and historical notes, exercises
- Probabilistic reasoning:
- Representing knowledge in an uncertain domain
- Semantics of Bayesian networks
- Efficient representation of conditional distributions
- Exact inference in Bayesian networks
- Approximate inference in Bayesian networks
- Relational and first-order probability models
- Other approaches to uncertain reasoning
- Summary, bibliographical and historical notes, exercises
- Probabilistic reasoning over time:
- Time an uncertainty
- Inference in temporal models
- Hidden markov models
- Kalman filters
- Dynamic Bayesian networks
- Keeping track of many objects
- Summary, bibliographical and historical notes, exercises
- Making simple decisions:
- Combining beliefs and desires under uncertainty
- Basis of utility theory
- Utility functions
- Multiattribute utility functions
- Decision networks
- Value of information
- Decision-theoretic expert systems
- Summary, bibliographical and historical notes, exercises
- Making complex decisions:
- Sequential decision problems
- Value iteration
- Policy iteration
- Partially observable MDPs
- Decisions with multiple agents: game theory
- Mechanism design
- Summary, bibliographical and historical notes, exercises
- Learning:
- Learning from examples:
- Forms of learning
- Supervised learning
- Learning decision trees
- Evaluating and choosing the best hypothesis
- Theory of learning
- Regression and classification with linear models
- Artificial neural networks
- Nonparametric models
- Support vector machines
- Ensemble learning
- Practical machine learning
- Summary, bibliographical and historical notes, exercises
- Knowledge in learning:
- Logical formulation of learning
- Knowledge in learning
- Explanation-based learning
- Learning using relevance information
- Inductive logic programming
- Summary, bibliographical and historical notes, exercises
- Learning probabilistic models:
- Statistical learning
- Learning with complete data
- Learning with hidden variables: the EM algorithm
- Summary, bibliographical and historical notes, exercises
- Reinforcement learning:
- Introduction
- Passive reinforcement learning
- Active reinforcement learning
- Generalization in reinforcement learning
- Policy search
- Applications of reinforcement learning
- Summary, bibliographical and historical notes, exercises
- Communicating, Perceiving, And Acting:
- Natural language processing:
- Language models
- Text classification
- Information retrieval
- Information extraction
- Summary, bibliographical and historical notes, exercises
- Natural language for communication:
- Phrase structure grammars
- Syntactic analysis (parsing)
- Augmented grammars and semantic interpretation
- Machine translation
- Speech recognition
- Summary, bibliographical and historical notes, exercises
- Perception:
- Image formation
- Early image-processing operations
- Object recognition by appearance
- Reconstructing the 3D world
- Object recognition for structural information
- Using vision
- Summary, bibliographical and historical notes, exercises
- Robotics:
- Introduction
- Robot hardware
- Robotic perception
- Planning to move
- Planning uncertain movements
- Moving
- Robotic software architectures
- Application domains
- Summary, bibliographical and historical notes, exercises
- Conclusions:
- Philosophical foundations:
- Weak AI: can machines act intelligently?
- Strong AI: can machines really think?
- Ethics and risks of developing artificial intelligence
- Summary, bibliographical and historical notes, exercises
- AI: the present and future:
- Agent components
- Agent architectures
- Are we going in the right direction?
- What if AI does succeed?
- Mathematical background:
- Complexity analysis and O() notation
- Vectors, matrices, and linear algebra
- Probability distribution
- Notes on languages and algorithms:
- Defining languages with Backus-Naur Form (BNF)
- Describing algorithms with pseudocode
- Online help
- Bibliography
- Index